Data Prepatation

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import plotly.graph_objects as go
import plotly.express as px
import os
import warnings
warnings.filterwarnings('ignore')

datasource: https://www.kaggle.com/datasets/imdevskp/corona-virus-report

files = os.listdir(r"Enter absolute path")

files

['country_wise_latest.csv',
 'covid_19_clean_complete.csv',
 'day_wise.csv',
 'full_grouped.csv',
 'usa_county_wise.csv',
 'worldometer_data.csv']

path = r"Enter absolute path"

read_data = lambda path, filename: pd.read_csv(path + "/" + filename)

country_wise_data = read_data(path, "country_wise_latest.csv")

country_wise_data.head()

	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	Confirmed last week	1 week change	1 week % increase	WHO Region
0	Afghanistan	36263	1269	25198	9796	106	10	18	3.50	69.49	5.04	35526	737	2.07	Eastern Mediterranean
1	Albania	4880	144	2745	1991	117	6	63	2.95	56.25	5.25	4171	709	17.00	Europe
2	Algeria	27973	1163	18837	7973	616	8	749	4.16	67.34	6.17	23691	4282	18.07	Africa
3	Andorra	907	52	803	52	10	0	0	5.73	88.53	6.48	884	23	2.60	Europe
4	Angola	950	41	242	667	18	1	0	4.32	25.47	16.94	749	201	26.84	Africa

Covid_19_clean_complete = read_data(path, "covid_19_clean_complete.csv")

Covid_19_clean_complete.head()

	Province/State	Country/Region	Lat	Long	Date	Confirmed	Deaths	Recovered	Active	WHO Region
0	NaN	Afghanistan	33.93911	67.709953	2020-01-22	0	0	0	0	Eastern Mediterranean
1	NaN	Albania	41.15330	20.168300	2020-01-22	0	0	0	0	Europe
2	NaN	Algeria	28.03390	1.659600	2020-01-22	0	0	0	0	Africa
3	NaN	Andorra	42.50630	1.521800	2020-01-22	0	0	0	0	Europe
4	NaN	Angola	-11.20270	17.873900	2020-01-22	0	0	0	0	Africa

day_wise = read_data(path, "day_wise.csv")

day_wise.head()

	Date	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	Deaths / 100 Cases	Recovered / 100 Cases	Deaths / 100 Recovered	No. of countries
0	2020-01-22	555	17	28	510	0	0	0	3.06	5.05	60.71	6
1	2020-01-23	654	18	30	606	99	1	2	2.75	4.59	60.00	8
2	2020-01-24	941	26	36	879	287	8	6	2.76	3.83	72.22	9
3	2020-01-25	1434	42	39	1353	493	16	3	2.93	2.72	107.69	11
4	2020-01-26	2118	56	52	2010	684	14	13	2.64	2.46	107.69	13

fully_grouped_data = read_data(path, 'full_grouped.csv')

fully_grouped_data.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
0	2020-01-22	Afghanistan	0	0	0	0	0	0	0	Eastern Mediterranean
1	2020-01-22	Albania	0	0	0	0	0	0	0	Europe
2	2020-01-22	Algeria	0	0	0	0	0	0	0	Africa
3	2020-01-22	Andorra	0	0	0	0	0	0	0	Europe
4	2020-01-22	Angola	0	0	0	0	0	0	0	Africa

worldmeter_data = read_data(path, 'worldometer_data.csv')

worldmeter_data.head()

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia
3	Russia	Europe	1.459409e+08	871894	NaN	14606.0	NaN	676357.0	NaN	180931.0	2300.0	5974.0	100.0	29716907.0	203623.0	Europe
4	South Africa	Africa	5.938157e+07	538184	NaN	9604.0	NaN	387316.0	NaN	141264.0	539.0	9063.0	162.0	3149807.0	53044.0	Africa

Out of all these data sets we use worldmeter_data as this consists of all the data that is required and new parameters can be derived

Trend of the data cases

px.line(day_wise,x="Date",y=["Active", "Recovered","Confirmed","Deaths"],title="Trend of covid 19",template="plotly_dark",
       labels= {"value": "Covid Cases"}).show()

we will try to answer the top 10 countries with maximum Total cases

Total Covid cases

#### We can get this data from worldmeter_data, which has Country/region. total population, toal cases, Total deaths, Active cases
sns.set(style="whitegrid")
plt.figure(figsize=(10,5))
sns.barplot(data = worldmeter_data.sort_values(by = "TotalCases", ascending = False).head(10), x = "Country/Region", y = "TotalCases")\
    .set(xlabel='Top 10 Countries', ylabel='Total Number of Covid cases in Millions', title = "Top 10 countries with total number of active cases");

Total Deaths

sns.set(style="whitegrid")
plt.figure(figsize=(10,5))
sns.barplot(data = worldmeter_data.sort_values(by= "TotalDeaths", ascending = False).head(10), x = "Country/Region", y = "TotalDeaths")\
    .set(xlabel='Top 10 Countries', ylabel='Total Number of Covid Deaths', title = "Top 10 conuntries with total number of Covid Deaths");

Total Active Cases

Total_active_cases = px.bar(worldmeter_data.sort_values(by= "ActiveCases", ascending = False).head(10), x = "Country/Region", y = "ActiveCases", template="seaborn",title="Top 10 countries with Total Number of Covid Active Cases",
                           width=800, height=300)
Total_active_cases.show()

# can we say the USA, Brazil, India, Russia are more effected contries by lokking at Total number of cases?
# No we are not lokking the percapita deta hence we cannot make make any colclusions about it
# Plotting the grapth to understand the senario, considering Tot Cases/1M pop, Deaths/1M pop
worldmeter_data.columns

Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region'],
      dtype='object')

Total cases/1M population

px.bar(worldmeter_data.sort_values(by= "Tot Cases/1M pop", ascending = False).head(10), x = "Country/Region", y = "Tot Cases/1M pop", template="plotly",title="<b>Top 10 countries with Total Number of Cases/1M population<b>",
      labels={"Tot Cases/1M pop": "Cases/1M Population"}, height=400, width=800).show()

Even though the total number of cases in USA is very high, Qatar has more number of total covid cases when normalize the cases one total population

Deaths/1M pop

px.bar(worldmeter_data.sort_values(by= "Tests/1M pop", ascending = False).head(10), x = "Country/Region", y = "Tests/1M pop", template="presentation",title="<b>Top 10 countries with maximum deaths/1Million </b>").show()

Even tough we have more number of covid cases in countries like USA, Brazil, India. Recovery was good, covid spread/Milliion is less and less number of deaths/Million are recorded

worldmeter_data.columns

Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region'],
      dtype='object')

We can also represent the data with treemap

px.treemap(worldmeter_data.head(10),values="TotalCases",path=['Country/Region'],template="presentation", title = "Top 10 countries with maximum number of Covid cases").show()

We can visualize the percentage of cases across all the countries with the above tree map

Top 10 countries with Total Confirmed Cases, Total Recovered Cases, Total Deaths,Total Active Cases

fig=px.bar(worldmeter_data.head(10),y='Country/Region',x='TotalCases',color='TotalCases',text="TotalCases")
fig.update_layout(template="plotly_dark",title_text="Top 10 countries with Total confirmed cases", height = 350, width = 800)
fig.show()

px.bar(worldmeter_data.sort_values(by="TotalCases", ascending=False).head(10),x='Country/Region',y=['Serious,Critical','TotalDeaths','TotalRecovered','ActiveCases','TotalCases'],template="simple_white", title="Top 10 countries covid spread report",
      labels={"value": "Covid Cases"})\
    .show()

At one Glance data visualization is possbile with stacked bar graphs

Finding the percentage of tests covered for each country

We can get the percentage by total tests / population

worldmeter_data["PercentageofTotalTests"] = (worldmeter_data['TotalTests']/worldmeter_data['Population'])*100

worldmeter_data['PercentageofTotalTests'].idxmax()

91

worldmeter_data.iloc[worldmeter_data['PercentageofTotalTests'].idxmax(), :]

Country/Region            Luxembourg
Continent                     Europe
Population                  626952.0
TotalCases                      7073
NewCases                         NaN
TotalDeaths                    119.0
NewDeaths                        NaN
TotalRecovered                5750.0
NewRecovered                     NaN
ActiveCases                   1204.0
Serious,Critical                 9.0
Tot Cases/1M pop             11282.0
Deaths/1M pop                  190.0
TotalTests                  623994.0
Tests/1M pop                995282.0
WHO Region                    Europe
PercentageofTotalTests     99.528194
Name: 91, dtype: object

Luxembourg is the country where almost everyone went through covid 19 test

Now we plot the grapth for percentage of total tests conducted

fig=px.bar(worldmeter_data.sort_values(by = 'PercentageofTotalTests', ascending=False).head(10) ,color='PercentageofTotalTests',y="Country/Region",x='PercentageofTotalTests',template="plotly_dark",title="Top 10 Countries with higest percentage of tests")
fig.show()

px.bar(worldmeter_data.sort_values(by = 'PercentageofTotalTests', ascending=False),color='PercentageofTotalTests',y="Country/Region",x='PercentageofTotalTests',template="plotly_dark",title="Percentage of covid tests done tests with respect to total Population", width=1500, height=1000).show()

if percentage of total tests are very less there should be chance that more number of total cases, active cases in a conutry

Understanding the data with ratios

Critical/Serious covid case to deaths

worldmeter_data["Critical_to_death_Percentage"] = worldmeter_data['Serious,Critical']/worldmeter_data['TotalDeaths']

worldmeter_data["Critical_to_death_Percentage"].max()

1.6

worldmeter_data["Critical_to_death_Percentage"]

0      0.112381
1      0.084323
2      0.214804
3      0.157470
4      0.056122
         ...   
204         NaN
205         NaN
206         NaN
207         NaN
208         NaN
Name: Critical_to_death_Percentage, Length: 209, dtype: float64

sns.set_theme(style="whitegrid")
plt.figure(figsize = (10, 5))
sns.barplot(data = worldmeter_data.sort_values(by = "Critical_to_death_Percentage", ascending=False).head(10),orient = 'h', x = "Critical_to_death_Percentage", y = 'Country/Region')\
    .set(title = "Critical covid cases to total death ratio")
plt.xlabel("Critical to deaths ratio")
plt.ylabel("Countries")
plt.show()

In most instances critical cases are converted into deaths for the countries Namibia, Turk, uzbekisthan compared to others`

Deaths to recovered ratio

worldmeter_data['Deaths_to_recovered_ratio'] = worldmeter_data['TotalDeaths']/worldmeter_data['TotalRecovered']

sns.set_theme(style="whitegrid")
plt.figure(figsize = (15, 5))
sns.barplot(data = worldmeter_data.sort_values(by = "Deaths_to_recovered_ratio", ascending=False).head(10),orient = 'v', y = "Deaths_to_recovered_ratio", x = 'Country/Region')\
    .set(title = "Total deaths to total recovered ratio")
plt.xlabel("Deaths to recovered ratio")
plt.ylabel("Countries")
plt.show()

More than 50% of chance that the perople from Yemen Belgium can dead when compared with the recovered cases

Tests to confirmed ratio

worldmeter_data['Tests_to_confirmed_ratio'] = worldmeter_data['TotalTests']/worldmeter_data['TotalCases']

(worldmeter_data['TotalTests']/worldmeter_data['TotalCases']).max()

1468.7

sns.set_theme(style="whitegrid")
plt.figure(figsize = (15, 5))
sns.barplot(data = worldmeter_data.sort_values(by = "Tests_to_confirmed_ratio", ascending=False).head(10),orient = 'v', y = "Tests_to_confirmed_ratio", x = 'Country/Region')\
    .set(title = "Tests to confirmed ratio")
plt.ylabel("Tests to confirmed ratio")
plt.xlabel("Countries")
plt.show()

worldmeter_data.columns

Index(['Country/Region', 'Continent', 'Population', 'TotalCases', 'NewCases',
       'TotalDeaths', 'NewDeaths', 'TotalRecovered', 'NewRecovered',
       'ActiveCases', 'Serious,Critical', 'Tot Cases/1M pop', 'Deaths/1M pop',
       'TotalTests', 'Tests/1M pop', 'WHO Region', 'PercentageofTotalTests',
       'Critical_to_death_Percentage', 'Deaths_to_recovered_ratio',
       'Tests_to_confirmed_ratio'],
      dtype='object')

px.choropleth(data_frame=worldmeter_data, locations='Country/Region', locationmode="country names", color="TotalCases", template="presentation", hover_data=["ActiveCases", "TotalCases", "TotalDeaths", "Serious,Critical", "TotalRecovered", "PercentageofTotalTests"],
              color_continuous_scale = px.colors.sequential.matter,
              title='Covid 19 Spread across all the countries').show()

We can get data for each country as follows

worldmeter_data.head()

	Country/Region	Continent	Population	TotalCases	NewCases	TotalDeaths	NewDeaths	TotalRecovered	NewRecovered	ActiveCases	Serious,Critical	Tot Cases/1M pop	Deaths/1M pop	TotalTests	Tests/1M pop	WHO Region	PercentageofTotalTests	Critical_to_death_Percentage	Deaths_to_recovered_ratio	Tests_to_confirmed_ratio
0	USA	North America	3.311981e+08	5032179	NaN	162804.0	NaN	2576668.0	NaN	2292707.0	18296.0	15194.0	492.0	63139605.0	190640.0	Americas	19.063998	0.112381	0.063184	12.547170
1	Brazil	South America	2.127107e+08	2917562	NaN	98644.0	NaN	2047660.0	NaN	771258.0	8318.0	13716.0	464.0	13206188.0	62085.0	Americas	6.208521	0.084323	0.048174	4.526446
2	India	Asia	1.381345e+09	2025409	NaN	41638.0	NaN	1377384.0	NaN	606387.0	8944.0	1466.0	30.0	22149351.0	16035.0	South-EastAsia	1.603463	0.214804	0.030230	10.935742
3	Russia	Europe	1.459409e+08	871894	NaN	14606.0	NaN	676357.0	NaN	180931.0	2300.0	5974.0	100.0	29716907.0	203623.0	Europe	20.362285	0.157470	0.021595	34.083165
4	South Africa	Africa	5.938157e+07	538184	NaN	9604.0	NaN	387316.0	NaN	141264.0	539.0	9063.0	162.0	3149807.0	53044.0	Africa	5.304352	0.056122	0.024796	5.852658

def country_wisegeoMap(world_data, country):
    for c in country:
        df = world_data[world_data['Country/Region'] == c]
        f = px.choropleth(data_frame=df, locations='Country/Region', locationmode="country names", template="presentation", hover_data=["ActiveCases", "TotalCases", "TotalDeaths", "Serious,Critical", "TotalRecovered", "PercentageofTotalTests"],
              color_continuous_scale = px.colors.sequential.matter,
              title=f'Covid 19 Spread in {c}')
        f.show()
        
    

worldmeter_data['Country/Region'].unique().tolist()

['USA',
 'Brazil',
 'India',
 'Russia',
 'South Africa',
 'Mexico',
 'Peru',
 'Chile',
 'Colombia',
 'Spain',
 'Iran',
 'UK',
 'Saudi Arabia',
 'Pakistan',
 'Bangladesh',
 'Italy',
 'Turkey',
 'Argentina',
 'Germany',
 'France',
 'Iraq',
 'Philippines',
 'Indonesia',
 'Canada',
 'Qatar',
 'Kazakhstan',
 'Egypt',
 'Ecuador',
 'Bolivia',
 'Sweden',
 'Oman',
 'Israel',
 'Ukraine',
 'Dominican Republic',
 'Panama',
 'Belgium',
 'Kuwait',
 'Belarus',
 'UAE',
 'Romania',
 'Netherlands',
 'Singapore',
 'Guatemala',
 'Portugal',
 'Poland',
 'Nigeria',
 'Honduras',
 'Bahrain',
 'Japan',
 'Armenia',
 'Ghana',
 'Kyrgyzstan',
 'Afghanistan',
 'Switzerland',
 'Algeria',
 'Azerbaijan',
 'Morocco',
 'Uzbekistan',
 'Serbia',
 'Moldova',
 'Ireland',
 'Kenya',
 'Venezuela',
 'Nepal',
 'Austria',
 'Costa Rica',
 'Ethiopia',
 'Australia',
 'El Salvador',
 'Czechia',
 'Cameroon',
 'Ivory Coast',
 'S. Korea',
 'Denmark',
 'Palestine',
 'Bosnia and Herzegovina',
 'Bulgaria',
 'Madagascar',
 'Sudan',
 'North Macedonia',
 'Senegal',
 'Norway',
 'DRC',
 'Malaysia',
 'French Guiana',
 'Gabon',
 'Tajikistan',
 'Guinea',
 'Haiti',
 'Finland',
 'Zambia',
 'Luxembourg',
 'Mauritania',
 'Paraguay',
 'Albania',
 'Lebanon',
 'Croatia',
 'Djibouti',
 'Greece',
 'Libya',
 'Equatorial Guinea',
 'Maldives',
 'CAR',
 'Hungary',
 'Malawi',
 'Zimbabwe',
 'Nicaragua',
 'Hong Kong',
 'Congo',
 'Montenegro',
 'Thailand',
 'Somalia',
 'Mayotte',
 'Eswatini',
 'Sri Lanka',
 'Cuba',
 'Cabo Verde',
 'Namibia',
 'Mali',
 'Slovakia',
 'South Sudan',
 'Slovenia',
 'Lithuania',
 'Estonia',
 'Mozambique',
 'Rwanda',
 'Suriname',
 'Guinea-Bissau',
 'Benin',
 'Iceland',
 'Sierra Leone',
 'Yemen',
 'Tunisia',
 'New Zealand',
 'Angola',
 'Uruguay',
 'Latvia',
 'Jordan',
 'Liberia',
 'Uganda',
 'Cyprus',
 'Georgia',
 'Burkina Faso',
 'Niger',
 'Togo',
 'Syria',
 'Jamaica',
 'Malta',
 'Andorra',
 'Chad',
 'Gambia',
 'Sao Tome and Principe',
 'Botswana',
 'Bahamas',
 'Vietnam',
 'Lesotho',
 'Diamond Princess',
 'San Marino',
 'Réunion',
 'Channel Islands',
 'Guyana',
 'Tanzania',
 'Taiwan',
 'Comoros',
 'Burundi',
 'Myanmar',
 'Mauritius',
 'Isle of Man',
 'Mongolia',
 'Eritrea',
 'Guadeloupe',
 'Martinique',
 'Faeroe Islands',
 'Aruba',
 'Cambodia',
 'Trinidad and Tobago',
 'Cayman Islands',
 'Gibraltar',
 'Papua New Guinea',
 'Sint Maarten',
 'Bermuda',
 'Brunei ',
 'Barbados',
 'Turks and Caicos',
 'Seychelles',
 'Monaco',
 'Bhutan',
 'Antigua and Barbuda',
 'Liechtenstein',
 'Belize',
 'French Polynesia',
 'St. Vincent Grenadines',
 'Saint Martin',
 'Macao',
 'Curaçao',
 'Fiji',
 'Saint Lucia',
 'Timor-Leste',
 'Grenada',
 'New Caledonia',
 'Laos',
 'Dominica',
 'Saint Kitts and Nevis',
 'Greenland',
 'Montserrat',
 'Caribbean Netherlands',
 'Falkland Islands',
 'Vatican City',
 'Western Sahara']

country_wisegeoMap(worldmeter_data, ['India', "USA"])

If we need the data for each date, we can get it as follows

from plotly.subplots import make_subplots
import plotly.graph_objects as go

fully_grouped_data.head()

	Date	Country/Region	Confirmed	Deaths	Recovered	Active	New cases	New deaths	New recovered	WHO Region
0	2020-01-22	Afghanistan	0	0	0	0	0	0	0	Eastern Mediterranean
1	2020-01-22	Albania	0	0	0	0	0	0	0	Europe
2	2020-01-22	Algeria	0	0	0	0	0	0	0	Africa
3	2020-01-22	Andorra	0	0	0	0	0	0	0	Europe
4	2020-01-22	Angola	0	0	0	0	0	0	0	Africa

def country_visualization(fully_grouped_data,country):
    
    grouped_df=fully_grouped_data[fully_grouped_data['Country/Region']==country]
    grouped_df=grouped_df.loc[:,['Date',"Active", 'Recovered',"Confirmed", 'Deaths']]
    plot = make_subplots(rows=1, cols=4,subplot_titles=("Active_Cases", 'Recovered_cases',"Confirmed_cases", 'Deaths'))
    plot.add_trace(
        go.Bar(name="Active",x=grouped_df['Date'],y=grouped_df['Active']),
        row=1, col=1
    )

    plot.add_trace(
        go.Bar(name="Recovered",x=grouped_df['Date'],y=grouped_df['Recovered']),
        row=1, col=2
    )
    plot.add_trace(
        go.Bar(name="Confirmed",x=grouped_df['Date'],y=grouped_df['Confirmed']),
        row=1, col=3
    )

    plot.add_trace(
        go.Bar(name="Deaths",x=grouped_df['Date'],y=grouped_df['Deaths']),
        row=1, col=4
    )

    plot.update_layout(title_text=f"Covid Spread in {country}",template="presentation")
    plot.show()

country_visualization(fully_grouped_data,'India')

country_visualization(fully_grouped_data,'China')